Fault diagnosis method and device of power transformer

Abstract

The invention discloses a fault diagnosis method and a fault diagnosis device of a power transformer. The method comprises the following steps: establishing a state characteristic data table based on an in-oil dissolved gas sample with a definite fault type; carrying out normalized treatment on the state characteristic data table and establishing a normalized fault table; calculating based on the normalized fault table to obtain various fault type clustering centers; based on the clustering centers, establishing a state standard spectrum matrix; calculating through an improved main component analysis method to obtain a characteristic value, a characteristic vector and a main component contribution rate; setting a threshold value and correspondingly selecting a main component; and calculating an Euler distance between a sample to be detected and the main component of a state characteristic sample main component and taking a state characteristic sample corresponding to a minimum distance value as a diagnosis result. The fault diagnosis method and device of the power transformer have the following advantages that a state standard spectrum is calculated by utilizing fuzzy clustering, and subject data removal and sample quantity restriction are avoided; meanwhile, the dimension of the data can be reduced and main characteristics for representing fault types are refined; and the accuracy of latent fault diagnosis in the power transformer is effectively improved.

Description

technical field [0001] The invention relates to the technical field of power transformer fault diagnosis, in particular to a power transformer fault diagnosis method and device. Background technique [0002] With the rapid development of my country's economy and the improvement of power technology, the power industry is developing towards high voltage, large capacity, and intelligence. While the number of power equipment is increasing, the failure rate of equipment is also showing an upward trend. As an important equipment in the power system, the power transformer undertakes the functions of voltage conversion, power transmission and distribution, and its operating status directly affects the safety and stability of the entire power system. Due to the complex internal structure of the power transformer and its special operating environment, it is subjected to physical, chemical, electrical, mechanical, temperature, environmental and other stresses, and considering many fact...

AI Technical Summary

Problems solved by technology

However, the most commonly used method based on compositional ratios in DGA has problems such as too absolute coding boundaries, blind ratios, misdiagnosis, and application under specific content conditions.

With the development of various artificial intelligence technologies, intelligent methods such as artificial neural networks, fuzzy theory, support vector machines, and data fusion have achieved some application results in the field of power transformer fault diagnosis. However, the existence of SVM requires the construction of classifiers, classification efficiency Low-level problems, ANN is prone to oscillations and falls into local optimal values, while the subjectivity of fuzzy theory and problems of ignoring attribute correlations, etc.

The consistency and reliability of the diagnosis results are caused by the redundancy of fault information, the determination of the standard fault type is subject to human subjective data elimination, and the fuzziness and uncertainty of the relationship between the power transformer insulation fault type and the dissolved gas content. The problem has not been handled well, so the diagnostic efficiency and accuracy need to be further improved

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